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Calibration trainer
Give 90% ranges for quantities computed fresh each round, then see how many held the truth, a calibration curve, and your hits round by round.
In short
Being calibrated means that the things you are 90% sure of turn out true about 90% of the time. The trainer asks for 90% ranges on quantities it computes fresh every round, shows how many of your ranges held the true value, and keeps a running total with a calibration curve. Most people start with ranges that are far too narrow.
How the trainer works
Each round is five questions. For each you give a low and a high number such that you are 90% sure the truth lies between them. The questions are computed rather than looked up: counts of drawn dots, powers, square roots, compound interest, committee counts, primes below a number. So every truth is computed, not remembered or looked up, and a new round never repeats an old one.
After each round you see the truths, your hits in that round, and a running total across rounds. The running total is what matters: five questions bounce around, and twenty start to say something.
The curve and the badge
The calibration curve reads each of your ranges as the middle 90% of a bell curve on a log scale, then asks how many truths would have fallen inside at levels from 50% to 99%. If you are calibrated, the dots follow the diagonal; if they sit below it, your ranges were too narrow. That bell-curve reading is an assumption about what a range means, stated so you can weigh it.
The trainer shows a Calibrated badge once you have given at least 20 ranges in one visit and between 80% and 95% of them held the true value. It is a statement about those ranges, not about you.
Why practise this
Interval estimates are one of the most consistently overconfident judgements in the research literature: in studies such as Soll and Klayman's, ranges meant to hold the truth 80% or 90% of the time held it far less often. Training helps. In a large forecasting tournament, Mellers and colleagues found that a short training module on probabilistic reasoning improved forecasters' accuracy.
Sources
- Soll and Klayman (2004), Overconfidence in interval estimates, Journal of Experimental Psychology: Learning, Memory, and Cognition
- Moore and Healy (2008), The trouble with overconfidence, Psychological Review
- Mellers and colleagues (2014), Psychological strategies for winning a geopolitical forecasting tournament, Psychological Science
Text, questions and code on this page are original to MyTestAtlas. The cited studies describe the classic procedure; none of their items is reproduced.